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Fast, accurate codebase intelligence for AI coding agents.
ctx++ is an MCP (Model Context Protocol) server that gives AI agents precise, structured understanding of large codebases. It extracts symbols using native tree-sitter parsing, indexes them in SQLite with both full-text and vector search, and traces call graphs to automatically map how features are assembled across files -- no hand-maintained documentation required.
A local MCP server that gives AI agents fast symbol search, call-graph traversal, and blast-radius analysis over your codebase.
ctx++ is built around three principles:
Want to add another language? See docs/ADDING-LANGUAGE-SUPPORT.md for a step-by-step implementation template and PR checklist.
# Install Ollama, then pull the default embedding model:
ollama pull bge-m3
Without Ollama, ctx++ still works but provides keyword search only. Semantic search and feature traversal quality depend on embeddings.
go install github.com/cavenine/ctxpp@latest
Or build from source:
git clone https://github.com/cavenine/ctxpp
cd ctxpp
make build
ctxpp index --path /path/to/your/project
This creates .ctxpp/index.db in the project root. Add it to .gitignore:
.ctxpp/
Subsequent runs only re-process changed files. Branch switches self-heal automatically via the file watcher.
If parser logic changes but your source files do not, force a full reparse of supported files:
ctxpp index --path /path/to/your/project --force
The examples below use Ollama with bge-m3 (the default). If Ollama is not running, omit CTXPP_OLLAMA_* — ctx++ will fall back to keyword search only.
OpenCode (opencode.json in project root):
{
"mcp": {
"ctxpp": {
"type": "local",
"command": ["ctxpp", "mcp"],
"enabled": true,
"environment": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_OLLAMA_URL": "http://localhost:11434",
"CTXPP_OLLAMA_MODEL": "bge-m3"
}
}
}
}
Claude Code (.mcp.json):
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_OLLAMA_URL": "http://localhost:11434",
"CTXPP_OLLAMA_MODEL": "bge-m3"
}
}
}
}
Cursor / Windsurf (.cursor/mcp.json or .windsurf/mcp.json):
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_OLLAMA_URL": "http://localhost:11434",
"CTXPP_OLLAMA_MODEL": "bge-m3"
}
}
}
}
Ask your AI agent anything about the codebase:
use ctxpp to show me everything involved in account authentication
use ctxpp to find where FetchAccount is defined and what calls it
use ctxpp_blast_radius to tell me what breaks if I change the Account struct
ctx++ uses Ollama for embedding-based semantic search. The default model is bge-m3 (BAAI's BGE-M3, 1024 dimensions), which was selected through head-to-head quality benchmarks against multiple models on real codebases.
ollama pull bge-m3
ctx++ auto-detects Ollama on localhost:11434 at startup. If Ollama is not running, ctx++ falls back to keyword search only and prints a warning.
To use a different embedding model (e.g., all-minilm for faster indexing at the cost of some search quality):
"environment": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_OLLAMA_MODEL": "all-minilm"
}
For environments without a local GPU, ctx++ can use Amazon Titan Text Embeddings V2 via AWS Bedrock. Quality is comparable to the Ollama/bge-m3 default (4.7/5 vs 4.8/5 on the kubernetes benchmark).
Prerequisites: AWS credentials configured via ~/.aws/credentials, AWS_PROFILE, or IAM role. The identity needs bedrock:InvokeModel permission on amazon.titan-embed-text-v2:0.
Set CTXPP_EMBED_BACKEND=bedrock and the following env vars:
OpenCode (opencode.json in project root):
{
"mcp": {
"ctxpp": {
"type": "local",
"command": ["ctxpp", "mcp"],
"enabled": true,
"environment": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_EMBED_BACKEND": "bedrock",
"CTXPP_BEDROCK_REGION": "us-east-1",
"CTXPP_BEDROCK_MODEL": "amazon.titan-embed-text-v2:0",
"CTXPP_BEDROCK_DIMS": "1024",
"CTXPP_EMBED_CONCURRENCY": "100"
}
}
}
}
Claude Code (.mcp.json):
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_EMBED_BACKEND": "bedrock",
"CTXPP_BEDROCK_REGION": "us-east-1",
"CTXPP_BEDROCK_MODEL": "amazon.titan-embed-text-v2:0",
"CTXPP_BEDROCK_DIMS": "1024",
"CTXPP_EMBED_CONCURRENCY": "100"
}
}
}
}
Cursor / Windsurf (.cursor/mcp.json or .windsurf/mcp.json):
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_EMBED_BACKEND": "bedrock",
"CTXPP_BEDROCK_REGION": "us-east-1",
"CTXPP_BEDROCK_MODEL": "amazon.titan-embed-text-v2:0",
"CTXPP_BEDROCK_DIMS": "1024",
"CTXPP_EMBED_CONCURRENCY": "100"
}
}
}
}
Or for initial indexing from the command line:
export CTXPP_EMBED_BACKEND=bedrock
export CTXPP_BEDROCK_REGION=us-east-1
export CTXPP_BEDROCK_MODEL=amazon.titan-embed-text-v2:0
export CTXPP_BEDROCK_DIMS=1024
export CTXPP_EMBED_CONCURRENCY=100 # increase to 200 for large repos
ctxpp index --path /path/to/your/project
Trade-offs vs Ollama:
Bedrock is the right choice for CI/CD pipelines, cloud-hosted agents, or developer machines without a GPU. For interactive development with a GPU available, Ollama is faster.
ctx++ can also use any provider that exposes the OpenAI POST /v1/embeddings API. This includes OpenAI, OpenAI-compatible proxies, vLLM, LiteLLM, LocalAI, and Ollama's OpenAI-compatible endpoint.
Set CTXPP_EMBED_BACKEND=openai and configure:
CTXPP_OPENAI_URLCTXPP_OPENAI_MODELCTXPP_OPENAI_DIMSCTXPP_OPENAI_API_KEY (optional for local unauthenticated servers)Example with OpenAI hosted embeddings:
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_EMBED_BACKEND": "openai",
"CTXPP_OPENAI_URL": "https://api.openai.com",
"CTXPP_OPENAI_MODEL": "text-embedding-3-small",
"CTXPP_OPENAI_DIMS": "1536",
"CTXPP_OPENAI_API_KEY": "${OPENAI_API_KEY}"
}
}
}
}
Example with Ollama's OpenAI-compatible endpoint:
export CTXPP_EMBED_BACKEND=openai
export CTXPP_OPENAI_URL=http://localhost:11434
export CTXPP_OPENAI_MODEL=bge-m3
export CTXPP_OPENAI_DIMS=1024
ctxpp index --path /path/to/your/project
This backend is opt-in only. Auto-detection still prefers TEI, then Ollama, then bundled fallback.
All configuration is via environment variables.
ctxpp index [--path/-p <path>] [--force] Index or reindex a project (default: $CTXPP_PROJECT or current directory)
ctxpp backfill [--path/-p <path>] Re-embed symbols missing embedding vectors
ctxpp mcp Start the MCP server over stdio
ctxpp version Print version
ctx++ is written in Go and built on:
modernc.org/sqlite (pure Go, no CGO) with FTS5 for full-text search and brute-force cosine similarity for vector searchbge-m3)The index lives in a single .ctxpp/index.db file per project. The schema tracks files, symbols, embeddings, call edges, and import edges. All queries hit indexed columns -- no full-table scans, no loading the entire index into memory.
See PRD.md for full architecture and design decisions.
When you ask ctxpp_feature_traverse about a symbol (e.g. "HandleLogin"):
This gives you the full call tree rooted at a symbol — useful for understanding what a function orchestrates without reading every file manually. Use ctxpp_blast_radius for the reverse direction: what calls this function?
MIT
A local MCP server that gives AI agents fast symbol search, call-graph traversal, and blast-radius analysis over your codebase.
Araçlar
Henüz araç listesi yok. Uzak sunucular ilk sağlık kontrolünden sonra bir liste alır.
SSS
Bu sayfadan Cursor, Claude Desktop, Claude Code veya VS Code için stdio yapılandırmasını kopyalayın. PlugMCP proxy'sini değil, yayıncının kurulumunu kullanır.
Bu kayıtta gerekli bir ortam değişkeni listelenmiyor.
Ctxpp yerel olarak stdio üzerinden çalışır.
Son kontrol: 2026-11-05. A local MCP server that gives AI agents fast symbol search, call-graph traversal, and blast-radius analysis over your codebase.
